1. Field of the Invention
Methods and apparatuses consistent with the present invention relate to a content recommending system, and more particularly, to a method and an apparatus for predicting a preference rating for content in order to recommend the content to a user, and a method and apparatus for selecting sample content necessary to predict a preference rating for the content.
2. Description of the Related Art
Various apparatuses, such as televisions (TVs), personal computers (PCs), personal media players (PMPs), and mobile phones, which are capable of reproducing content have come into widespread use. However, as the total number of pieces of content that a content providing system can possess exponentially increases, users can experience difficulty selecting desired content. Thus, content service providers manage a content recommending system in order to solve this problem. The content recommending system is designed to recommend content that the user would pay to watch, in consideration of his or her taste. To this end, this system analyzes the user's preference ratings for the content, selects content which the user would prefer to watch from a large number of pieces of content, and then recommends the selected content to the user. However, it is difficult to recommend content to a new user since the new user's preferences for the content are not yet known. Also, it is difficult to determine users to whom buying new content should be recommended since the new content has never been used.
In general, preference rating information regarding fifteen pieces of sample content is needed from the new user in order to analyze his or her preference rating for the content. Conventionally, content is randomly recommended to the new user in order to obtain the preference rating information therefor. However, since the content is recommended randomly without considering the new user's taste, it takes a long time for the new user to use and evaluate the recommended content and to provide data necessary for machine learning. If a plurality of pieces of content are selected and recommended to the new user according to popularity of the content and the preference rating information is obtained based on content that the new user selects from these recommended pieces of content, then the new user's own preference rating is difficult to determine. If a plurality of pieces of content that are very popular and preference rating information thereof, are recommended to the new user and preference rating information is obtained based on content selected by the new user from these pieces of content, then it is possible to obtain information to analyze the new user's preference for content but it takes a long time to perform machine learning.